ruff: enable and fix remaining rules

boxtree/bounding_box.py

@@ -158,11 +158,7 @@ def __call__(self, particles, radii, wait_for=None):
         from pytools import single_valued
         coord_dtype = single_valued(coord.dtype for coord in particles)
 
-        if radii is None:
-            radii_tuple = ()
-        else:
-            radii_tuple = (radii,)
-
+        radii_tuple = () if radii is None else (radii,)
         knl = self.get_kernel(dimensions, coord_dtype,
                 # have_radii:
                 radii is not None)

boxtree/cost.py

@@ -154,11 +154,7 @@ def make_pde_aware_translation_cost_model(dim, nlevels):
     p_fmm = np.array([var("p_fmm_lev%d" % i) for i in range(nlevels)])
     ncoeffs_fmm = (p_fmm + 1) ** (dim - 1)
 
-    if dim == 3:
-        uses_point_and_shoot = True
-    else:
-        uses_point_and_shoot = False
-
+    uses_point_and_shoot = dim == 3
     return FMMTranslationCostModel(
             ncoeffs_fmm_by_level=ncoeffs_fmm,
             uses_point_and_shoot=uses_point_and_shoot

boxtree/distributed/__init__.py

@@ -197,11 +197,7 @@ def make_distributed_wrangler(
 
     global_trav_dev, _ = traversal_builder(queue, global_tree_dev)
     global_trav_host = global_trav_dev.get(queue)
-
-    if tree_in_device_memory:
-        global_trav = global_trav_dev
-    else:
-        global_trav = global_trav_host
+    global_trav = global_trav_dev if tree_in_device_memory else global_trav_host
 
     # }}}
 

boxtree/pyfmmlib_integration.py

@@ -213,10 +213,8 @@ def wrapper(*args, **kwargs):
             def wrapper(*args, **kwargs):
                 kwargs["iffld"] = self.ifgrad
                 pot, fld = rout(*args, **kwargs)
-                if self.ifgrad:
-                    grad = -fld
-                else:
-                    grad = 0
+                grad = -fld if self.ifgrad else 0
+
                 return pot, grad
 
             # Doesn't work in in Py2
@@ -254,11 +252,7 @@ def wrapper(*args, **kwargs):
                 if (ier != 0).any():
                     raise RuntimeError(f"{name} failed with nonzero ier")
 
-                if self.ifgrad:
-                    grad = -fld
-                else:
-                    grad = 0
-
+                grad = -fld if self.ifgrad else 0
                 return pot, grad
 
             # Doesn't work in in Py2
@@ -604,11 +598,11 @@ def mem_estimate(order):
 
         ier, rotmatf = (
                 rotmat_builder(rotmat_order, m2l_rotation_angles))
-        assert (0 == ier).all()
+        assert (ier == 0).all()
 
         ier, rotmatb = (
                 rotmat_builder(rotmat_order, -m2l_rotation_angles))
-        assert (0 == ier).all()
+        assert (ier == 0).all()
 
         return (rotmatf, rotmatb, rotmat_order)
 

boxtree/tools.py

@@ -51,7 +51,7 @@ def realloc_array(queue, allocator, new_shape, ary, zero_fill=False, wait_for=No
     if wait_for is None:
         wait_for = []
 
-    if zero_fill:
+    if zero_fill:  # noqa: SIM108
         array_maker = cl.array.zeros
     else:
         array_maker = cl.array.empty
@@ -491,7 +491,7 @@ def __call__(self, queue, allocator, new_shape, ary, src_indices=None,
                 if debug:
                     assert int(cl.array.max(dst_indices).get()) < new_shape
 
-        if zero_fill:
+        if zero_fill:  # noqa: SIM108
             array_maker = cl.array.zeros
         else:
             array_maker = cl.array.empty
@@ -897,10 +897,7 @@ def with_queue(self, queue):
     def svm_capable(self):
         svm_capabilities = \
             self.queue.device.get_info(cl.device_info.SVM_CAPABILITIES)
-        if svm_capabilities & cl.device_svm_capabilities.FINE_GRAIN_BUFFER != 0:
-            return True
-        else:
-            return False
+        return svm_capabilities & cl.device_svm_capabilities.FINE_GRAIN_BUFFER != 0
 
     @property
     def host(self):

boxtree/tree.py

@@ -892,7 +892,7 @@ def link_point_sources(queue, tree, point_source_starts, point_sources,
 
     tree_attrs = {}
     for attr_name in tree.__class__.fields:
-        try:
+        try:  # noqa: SIM105
             tree_attrs[attr_name] = getattr(tree, attr_name)
         except AttributeError:
             pass

boxtree/tree_build.py

@@ -175,10 +175,7 @@ def __call__(self, queue, particles, kind="adaptive",
             raise ValueError(f"unknown tree kind '{kind}'")
 
         # we'll modify this below, so copy it
-        if wait_for is None:
-            wait_for = []
-        else:
-            wait_for = list(wait_for)
+        wait_for = [] if wait_for is None else list(wait_for)
 
         dimensions = len(particles)
 
@@ -301,10 +298,7 @@ def zeros(shape, dtype):
                         "dtype")
 
             def combine_srcntgt_arrays(ary1, ary2=None):
-                if ary2 is None:
-                    dtype = ary1.dtype
-                else:
-                    dtype = ary2.dtype
+                dtype = ary1.dtype if ary2 is None else ary2.dtype
 
                 result = empty(nsrcntgts, dtype)
                 if (ary1 is None) or (ary2 is None):
@@ -365,15 +359,15 @@ def combine_srcntgt_arrays(ary1, ary2=None):
             event, = refine_weights.events
             prep_events.append(event)
             max_leaf_refine_weight = max_particles_in_box
-        elif specified_refine_weights:
+        elif specified_refine_weights:  # noqa: SIM102
             if refine_weights.dtype != refine_weight_dtype:
                 raise TypeError(
                         f"refine_weights must have dtype '{refine_weight_dtype}'")
 
         if max_leaf_refine_weight < cl.array.max(refine_weights).get():
             raise ValueError(
                     "entries of refine_weights cannot exceed max_leaf_refine_weight")
-        if 0 > cl.array.min(refine_weights).get():
+        if cl.array.min(refine_weights).get() < 0:
             raise ValueError("all entries of refine_weights must be nonnegative")
         if max_leaf_refine_weight <= 0:
             raise ValueError("max_leaf_refine_weight must be positive")
@@ -607,10 +601,7 @@ def fin_debug(s):
 
         tree_build_proc = ProcessLogger(logger, "tree build")
 
-        if total_refine_weight > max_leaf_refine_weight:
-            level = 1
-        else:
-            level = 0
+        level = 1 if total_refine_weight > max_leaf_refine_weight else 0
 
         # INVARIANTS -- Upon entry to this loop:
         #
@@ -1461,7 +1452,7 @@ def make_arange(start, offset=level_used_box_count):
                 wait_for=wait_for)
             wait_for = [evt]
 
-            if srcntgts_have_extent:
+            if srcntgts_have_extent:  # noqa: SIM102
                 if debug:
                     assert (
                             box_srcntgt_counts_nonchild.get()
@@ -1471,7 +1462,7 @@ def make_arange(start, offset=level_used_box_count):
             if debug:
                 usi_host = user_source_ids.get()
                 assert (usi_host < nsources).all()
-                assert (0 <= usi_host).all()
+                assert (usi_host >= 0).all()
                 del usi_host
 
                 sti_host = srcntgt_target_ids.get()

boxtree/tree_build_kernels.py

@@ -1393,7 +1393,7 @@ def get_tree_build_kernel_info(context, dimensions, coord_dtype,
     """
 
     level_restrict = (kind == "adaptive-level-restricted")
-    adaptive = not (kind == "non-adaptive")
+    adaptive = (kind != "non-adaptive")
 
     # {{{ preparation
 

boxtree/visualization.py

@@ -171,6 +171,10 @@ def get_tikz_for_tree(self):
 # {{{ traversal plotting
 
 def _draw_box_list(tree_plotter, ibox, starts, lists, key_to_box=None, **kwargs):
+    rng = kwargs.pop("rng", None)
+    if rng is None:
+        rng = np.radnom.default_rng()
+
     default_facecolor = "blue"
 
     if key_to_box is not None:
@@ -183,7 +187,7 @@ def _draw_box_list(tree_plotter, ibox, starts, lists, key_to_box=None, **kwargs)
                     "edgecolor": getattr(kwargs, "facecolor", default_facecolor),
                     "fill": False,
                     "alpha": 0.5,
-                    "shrink_factor": -0.1+0.1*np.random.rand(),
+                    "shrink_factor": -0.1+0.1*rng.random(),
                     }
             tree_plotter.draw_box(ibox, **actual_kwargs)
             return
@@ -198,7 +202,7 @@ def _draw_box_list(tree_plotter, ibox, starts, lists, key_to_box=None, **kwargs)
             "facecolor": default_facecolor,
             "linewidth": 0,
             "alpha": 0.5,
-            "shrink_factor": 0.1 + np.random.rand()*0.2,
+            "shrink_factor": 0.1 + rng.random()*0.2,
             }
     actual_kwargs.update(kwargs)
     print(actual_kwargs["facecolor"], ibox, lists[start:end])

examples/cost_model.py

@@ -32,6 +32,7 @@ def demo_cost_model():
         Kernel,
     )
 
+    rng = np.random.default_rng(seed=42)
     nsources_list = [1000, 2000, 3000, 4000, 5000]
     ntargets_list = [1000, 2000, 3000, 4000, 5000]
     dims = 3
@@ -56,8 +57,9 @@ def fmm_level_to_order(tree, ilevel):
         targets = p_normal(queue, ntargets, dims, dtype, seed=18)
 
         from pyopencl.clrandom import PhiloxGenerator
-        rng = PhiloxGenerator(queue.context, seed=22)
-        target_radii = rng.uniform(
+
+        clrng = PhiloxGenerator(queue.context, seed=22)
+        target_radii = clrng.uniform(
             queue, ntargets, a=0, b=0.05, dtype=dtype
         ).get()
 
@@ -90,7 +92,7 @@ def fmm_level_to_order(tree, ilevel):
 
         timing_data = {}
         from boxtree.fmm import drive_fmm
-        src_weights = np.random.rand(tree.nsources).astype(tree.coord_dtype)
+        src_weights = rng.random(size=tree.nsources, dtype=tree.coord_dtype)
         drive_fmm(wrangler, (src_weights,), timing_data=timing_data)
 
         timing_results.append(timing_data)

pyproject.toml

@@ -78,25 +78,21 @@ extend-select = [
     "E",   # pycodestyle
     "F",   # pyflakes
     "G",   # flake8-logging-format
+    "I",   # flake8-isort
     "N",   # pep8-naming
     "NPY", # numpy
     "Q",   # flake8-quotes
-    "I",   # flake8-isort
-    "UP",  # pyupgrade
     "RUF", # ruff
-
-    # TODO
-    # "W",   # pycodestyle
-    # "SIM",
+    "SIM", # flake8-simplify
+    "UP",  # pyupgrade
+    "W",   # pycodestyle
 ]
 extend-ignore = [
     "C90",  # McCabe complexity
     "E221", # multiple spaces before operator
     "E226", # missing whitespace around arithmetic operator
     "E402", # module-level import not at top of file
-
-    # TODO
-    "NPY002", # legacy numpy rng
+    "SIM223", # simplify `False and ...` conditional
 ]
 
 [tool.ruff.lint.flake8-quotes]
@@ -119,12 +115,13 @@ known-local-folder = [
     "boxtree",
 ]
 lines-after-imports = 2
-# required-imports = ["from __future__ import annotations"]
 
-[tool.ruff.lint.per-file-ignores]
-"doc/**/*.py" = ["I002"]
-"examples/**/*.py" = ["I002"]
-"setup.py" = ["I002"]
+# TODO: enable postponed annotations at some point
+# required-imports = ["from __future__ import annotations"]
+# [tool.ruff.lint.per-file-ignores]
+# "doc/**/*.py" = ["I002"]
+# "examples/**/*.py" = ["I002"]
+# "setup.py" = ["I002"]
 
 [tool.typos.default]
 extend-ignore-re = [

test/test_cost_model.py

@@ -383,6 +383,8 @@ def test_estimate_calibration_params(actx_factory):
         Kernel,
     )
 
+    rng = np.random.default_rng(seed=42)
+
     nsources_list = [1000, 2000, 3000, 4000]
     ntargets_list = [1000, 2000, 3000, 4000]
     dims = 3
@@ -437,7 +439,7 @@ def fmm_level_to_order(tree, ilevel):
 
         timing_data = {}
         from boxtree.fmm import drive_fmm
-        src_weights = np.random.rand(tree.nsources).astype(tree.coord_dtype)
+        src_weights = rng.random(size=tree.nsources, dtype=tree.coord_dtype)
         drive_fmm(wrangler, (src_weights,), timing_data=timing_data)
 
         timing_results.append(timing_data)
@@ -564,7 +566,7 @@ def test_cost_model_op_counts_agree_with_constantone_wrangler(
 
     timing_data = {}
     from boxtree.fmm import drive_fmm
-    src_weights = np.random.rand(tree.nsources).astype(tree.coord_dtype)
+    src_weights = rng.random(size=tree.nsources, dtype=tree.coord_dtype)
     drive_fmm(wrangler, (src_weights,), timing_data=timing_data)
 
     cost_model = FMMCostModel(